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Dynamic classifier selection using clustering for spam detection
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Dynamic classifier selection using clustering for spam detection

Famil saeedian, M

Dynamic classifier selection using clustering for spam detection

Famil saeedian, M ; Sharif University of Technology | 2009

462 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/CIDM.2009.4938633
  3. Publisher: 2009
  4. Abstract:
  5. Most email users have encountered with spam problems, which have been addressed as a text classification or categorization problem. In this paper, we propose a novel spam detection method that uses ensemble of classifiers based on clustering and selection techniques. There is diversity in genre of e-mail's content and this method can find different topics in emails by clustering. It first computes disjoint clusters of emails, and then a classifier is trained on each cluster. When new email arrives, its cluster is identified. The classifier of the identified cluster is selected to classify the new email. Our method can extract many kinds of topics in emails. The evaluation shows that the algorithm outperforms majority voting. © 2009 IEEE
  6. Keywords:
  7. Spam ; Classification ; Classifier selection ; Clustering ; Ensemble ; Spam ; Artificial intelligence ; Classifiers ; Internet ; Learning systems ; Mining ; Spamming ; Text processing ; Electronic mail
  8. Source: 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, Nashville, TN, 30 March 2009 through 2 April 2009 ; 2009 , Pages 84-88 ; 9781424427659 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4938633